
'''simple docstring'''

import re
import string

import numpy as np

import datasets


UpperCAmelCase__ :			Tuple											=		"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

UpperCAmelCase__ :			int											=		"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

UpperCAmelCase__ :			str											=		"\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,   _KWARGS_DESCRIPTION     )
class  A  (						datasets.Metric     ):





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return datasets.MetricInfo(
				    description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"   ,				id="sequence"				),
				            "references": datasets.Value("string"   ,				id="sequence"				),
				        }				)   ,				reference_urls=[]   ,				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							int   ,				__magic_name__      :							Optional[Any]=None   ,				__magic_name__      :							Dict=False   ,				__magic_name__      :							Any=False   ,				__magic_name__      :							List[Any]=False   ,				):


				"""simple docstring"""

				if regexes_to_ignore is not None:
							for s in regexes_to_ignore:
										lowerCAmelCase__										=				np.array([re.sub(__magic_name__   ,				""   ,				__magic_name__				) for x in predictions]				)
										lowerCAmelCase__										=				np.array([re.sub(__magic_name__   ,				""   ,				__magic_name__				) for x in references]				)
				else:
							lowerCAmelCase__										=				np.asarray(__magic_name__				)
							lowerCAmelCase__										=				np.asarray(__magic_name__				)

				if ignore_case:
							lowerCAmelCase__										=				np.char.lower(__magic_name__				)
							lowerCAmelCase__										=				np.char.lower(__magic_name__				)

				if ignore_punctuation:
							lowerCAmelCase__										=				string.punctuation.maketrans(""   ,				""   ,				string.punctuation				)
							lowerCAmelCase__										=				np.char.translate(__magic_name__   ,				table=__magic_name__				)
							lowerCAmelCase__										=				np.char.translate(__magic_name__   ,				table=__magic_name__				)

				if ignore_numbers:
							lowerCAmelCase__										=				string.digits.maketrans(""   ,				""   ,				string.digits				)
							lowerCAmelCase__										=				np.char.translate(__magic_name__   ,				table=__magic_name__				)
							lowerCAmelCase__										=				np.char.translate(__magic_name__   ,				table=__magic_name__				)

				lowerCAmelCase__										=				predictions == references

				return {"exact_match": np.mean(__magic_name__				) * 100}












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






